Senior Member of Technical Staff, Safety and Security for Agents
Develops data generation, post-training, and evaluation methods to improve the safety, fairness, robustness, and security of large language models that can take actions in the world. The role requires strong statistics and software engineering skills, distributed LLM training experience, and expertise in data collection and ML evaluation.
About the job
Responsibilities
- Develop better, fairer, more trustworthy, and more secure large language models (LLMs), particularly models that can access external resources and take actions in the world.
- Design and implement data generation, post-training algorithms, and evaluation methods for model safety.
- Work closely with machine learning, data annotation, product, and policy teams.
- Design and conduct data collection tasks involving human annotators.
- Analyze datasets for quality, bias, and suitability for training machine learning models.
- Train LLMs on distributed training infrastructures.
- Evaluate and improve the generalizability and robustness of machine learning systems.
- Implement engineering solutions to test scientific hypotheses and analyze experimental results.
- Communicate findings effectively across cross-functional teams.
Requirements
- Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
- Extremely strong software engineering skills.
- Expertise in designing and conducting data collection tasks, including work with human annotators.
- Experience analyzing datasets for quality, biases, and suitability for training ML models.
- Hands-on experience training LLMs on distributed training infrastructures.
- Familiarity with evaluating and improving ML system generalizability and robustness.
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Excellent communication skills for cross-functional collaboration and presenting findings.
- One or more papers at top-tier venues such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, or EMNLP.
Compensation and Benefits
- Weekly lunch stipend of $75/£75 or equivalent in local currency.
- Full health and dental benefits, including a separate mental health budget.
- RRSP matching, 401(k), and pension scheme.
- 100% parental leave top-up for up to 6 months for either parent.
- Annual enrichment benefits covering arts and culture, fitness and wellness, quality time, and workspace improvements.
- Education and learning stipend for conferences, courses, and coaching.
- Six weeks of paid vacation (30 working days).
- Travel budget for remote employees to visit other offices and an annual company offsite.
- Coworking benefit for employees not near an office.
- $500 home office stipend.
Skills
Python, PyTorch, TensorFlow, JAX, LLMs, Distributed Training, Post-Training, Statistical Analysis, Experimental Design, Data Collection, Dataset Bias Analysis, Machine Learning Evaluation, Robustness Testing, Human Annotation, Software Engineering
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